Priors for the long run

Domenico Giannone, Michele Lenza and Giorgio Primiceri

No 832, Staff Reports from Federal Reserve Bank of New York

Abstract: We propose a class of prior distributions that discipline the long-run predictions of vector autoregressions (VARs). These priors can be naturally elicited using economic theory, which provides guidance on the joint dynamics of macroeconomic time series in the long run. Our priors for the long run are conjugate, and can thus be easily implemented using dummy observations and combined with other popular priors. In VARs with standard macroeconomic variables, a prior based on the long-run predictions of a wide class of theoretical models yields substantial improvements in the forecasting performance.

Keywords: Bayesian vector autoregressions; forecasting; overfitting; initial conditions; hierarchical models (search for similar items in EconPapers)
JEL-codes: C11 C32 C33 E37 (search for similar items in EconPapers)
Date: 2017-11-01
New Economics Papers: this item is included in nep-ecm, nep-for, nep-mac and nep-ore
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Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Priors for the Long Run (2019)
Working Paper: Priors for the long run (2018)
Working Paper: Priors for the Long Run (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fednsr:832

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